similar to: Goodness of Fit for Archimedean Copulas

Dear All, I'm now working on Archimedean copulas and try to test the goodness of fit. Which packages I should use? I have Clayton copula with parameter (5.35) and Frank (19.5). I found this build function wrote by Yan and Ivan via R Packages, but I'm not sure the matrix for x? Please advice. e.g gofCopula(claytonCopula(1),x) Thank you Regards, Fayyad [[alternative HTML version

Hi, Is anybody using Copula package for fitting copulas to own data? I have two marginals Log Normal with (parameters 1.17 and 0.76) and Gamma ( 2.7 and 1.05) Which package I should use to fit Gumbel and Clayton Copulas? Thanks, fayyad [[alternative HTML version deleted]]

Hi everybody, I try to simulate random numbers from a trivariate nested Archimedean copula. My aim is to correlate two processes with, e.g. theta2, as the so called child pair and then to correlate these two processes with a third one with theta1 (parent). This "figure" tries to capture what I am explaining theta1 theta2

Hi All, Are there any package to check the GOF for nonparametric copulas using R? Fayyad [[alternative HTML version deleted]]

Hi all, Has anyone used the qua.regressCOP2 function from the copBasic package??? The default copula function used in this function is plackett copula and I wanted to use archimedean copula. Attached below is my code: mycop<-frankCopula V=seq(0.001,0.99,by=0.000217) R<-qua.regressCOP2(0.25,V,cop=mycop,para=c(3.504)) And this is the error I get: Warning messages: 1: In

Hi all, I am trying to build a copula model using the Gumbel Copula and I have two marginal distributions.I know the marginal parameters by using the fitdistr() and optim().The problem is I dont know my copula parameter. I am getting a bit confused of how shall I go about it.I read the previous threads where the query was to estimate all the parameters.How shall I define my log-likelihood

Hi, I'm a new user of R. I'm using package copula implemented in R. I want to know how to test goodness of fit of student's t copula for 3 dimensional cases using real world data. In manual gof test has been performed on the copula families generated from rcopula function that i understood as random data generated from copula? How to perform the test with real/observed data matrix.

How can i calculate the discrete L2 distance between the empirical copula and the estimated one. -- View this message in context: http://www.nabble.com/Goodness-of-fit-tests-for-copula-tp17779318p17779318.html Sent from the R help mailing list archive at Nabble.com.

Can anyone advise me on some pratical papers or books On Gaussian Copulas? Anything in the genre of Copulas Dummies Would be a help. As simpe, and approachable with minimal pedantic style. Thanks, Neil -------------------------------------------------------- This information is being sent at the recipient's reques...{{dropped:16}}

I am trying to make a loglikelihood function using copulas. I am trying to use mvdc to find the density function. When I run this I got the error that the pdf and cdf of my function tobit doesn't exist. Can somebody guide me where my mistake is? dtobit <- function(beta,sigma, x, y) {ifelse(y>0, dnorm(y,x%*%beta, sigma),(1-pnorm((x%*%beta)/sigma)))} ptobit <- function(beta,sigma, x,

Dears, I am writing sice having question about your R package "copula". I am using this package to fit my data with some of the 22 copulas mentioned in Nelsen 1999. However, when trying random number generator I have got some difficulties. For example when I have familie number A13 where cupola is equal to C(u,v) = exp(1-((1-log(u))^theta+(1-log(v))^theta-1)^(1/theta)) devuv =

Hi, I found this document, but it concerns S+. If it could interest you'll see: http://faculty.washington.edu/ezivot/book/QuanCopula.pdf Cordially Vito You wrote: Dear R: Is there a function or a reference to simulate Gumbel copulas, please? Thanks in advance! Sincerely, Erin Hodgess mailto: hodgess at gator.uhd.edu R version 2.0.1 windows ===== Diventare costruttori di soluzioni

Dear R: Is there a function or a reference to simulate Gumbel copulas, please? Thanks in advance! Sincerely, Erin Hodgess mailto: hodgess at gator.uhd.edu R version 2.0.1 windows

I'm looking for goodness of fit tests for gamma distributions with large data sizes. I have a matrix with around 10,000 data values in it and i have fitted a gamma distribution over a histogram of the data. The problem is testing how well that distribution fits. Chi-squared seems to be used more for discrete distributions and kolmogorov-smirnov seems that large sample sizes make it had to

>>>>> jing hua zhao >>>>> on Mon, 24 Jun 2019 08:51:43 +0000 writes: > Hi All, > Thanks for all your comments which allows me to appreciate more of these in Python and R. > I just came across the matrixStats package, > ## EXAMPLE #1 > lx <- c(1000.01, 1000.02) > y0 <- log(sum(exp(lx))) > print(y0) ## Inf

Hi, Is anybody using Copula package for fitting copulas to own datas? I always got this message: Error in qnorm(p, mean, sd, lower.tail, log.p) : Non-numeric argument to mathematical function thanks, Psl _______________________________________________________________ Ossza meg bar?taival f?nyk?pen meg?r?k?tett ?lm?nyeit. http:\\fototar.t-online.hu

Dear R users, I've used the function qr.fit.sfn to estimate a quantile regression on a panel data set. Now I would like to compute an statistic to measure the goodness of fit of this model. Does someone know how could I do that? I could compute a pseudo R2 but in order to do that I would need the value of the objetive function at the optimum and I don't see how to get this from the

Hi there, I'm a newbie, plesae bear with me. I have a dataset with about 10000 ~ 30000 data points. Would like fit to both Gamma and Normal distribution to see which one fits better. How do I do this in R? Or I could do a normality test of the data, if it's normal, I then will do a normal fit, otherwise, a gamma fit. But again, I don't know how to do this either. Please help! David

All - Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Specifically, I'm wondering if anyone has implemented the goodness-of-fit process developed by Koenker and Machado (1999) for R? Though I have used package quantreg in the past, I may have overlooked this function, if it is included. Citation: Koenker, R. and

Hi R-Helpers , am working on nnet package.Multinom() has an option for finding the goodness of fit by giving the AIC value. Does nnet also gives some value to determine the accuracy. If not, can you guide me with some procedure to figure out the accuracy/goodness of fit of nnet model? Thanks in advance. -- View this message in context: